Eric T Bradlow

Eric T Bradlow
  • The K.P. Chao Professor
  • Professor of Marketing
  • Vice Dean of AI & Analytics at Wharton
  • Chairperson, Wharton Marketing Department
  • Professor of Economics; Professor of Education; Professor of Statistics and Data Science

Contact Information

  • office Address:

    761 Jon M. Huntsman Hall
    3730 Walnut Street
    University of Pennsylvania
    Philadelphia, PA 19104

Research Interests: Bayesian computation, latent variable models, marketing research methods, missing data problems, analytics, psychometrics

Links: CV, Twitter


Professor Eric T. Bradlow is the K.P. Chao Professor, Professor of Marketing, Statistics, Education and Economics, Chairperson of Wharton’s Marketing Department, and Vice Dean of AI & Analytics at Wharton. An applied statistician, Professor Bradlow uses high-powered statistical models to solve problems on everything from Internet search engines to product assortment issues. Specifically, his research interests include Bayesian modeling, statistical computing, and developing new methodology for unique data structures with application to business problems.

Eric is a fellow of the INFORMS Society for Marketing Science, a fellow of the American Statistical Association, a fellow of the American Educational Research Association, is past chair of the American Statistical Association Section on Statistics in Marketing, past Editor-in-Chief of Marketing Science, is a past statistical fellow of Bell Labs, and worked at DuPont Corporation’s Corporate Marketing and Business Research Division and the Educational Testing Service.

A prolific scholar, Professor Bradlow’s research has been published in top-tier academic journals such as the Journal of the American Statistical Association, Psychometrika, Statistica Sinica, Chance, Marketing Science, Management Science, and Journal of Marketing Research. He also serves as Associate Editor for the Journal of the American Statistical Association and the Journal of Marketing Research, and is on the Editorial Boards of Marketing Letters, Marketing Science, Journal of Marketing Research, Quantitative Marketing and Economics, and the Quarterly Journal of Electronic Commerce.

Professor Bradlow has won numerous teaching awards at Wharton, including the Linback Award for Distinguished PhD Teaching and Mentoring, the Anvil Award for MBA Education, MBA Core Curriculum teaching award, the Miller-Sherrerd MBA Core Teaching award and the Excellence in Teaching Award. His teaching interests include courses in Statistics, Marketing Research, Marketing Management and PhD Data Analysis, as well as any material related to customer analytics.

Professor Bradlow earned his PhD and Master’s degrees in Mathematical Statistics from Harvard University and his BS in Economics from the University of Pennsylvania.

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  • Mingyung Kim, Eric Bradlow, Raghuram Iyengar (Under Review), A Bayesian Dual-Network Clustering Approach for Selecting Data and Parameter Granularities.

    Abstract: While there are well-established methods for model selection (e.g., BIC, marginal likelihood), they generally condition on an a priori selected data (e.g., SKU-level data) and parameter granularity (e.g., brand-level parameters). That is, researchers think they are doing model selection, but what they are really doing is model selection conditional on their choices of data and parameter granularities. In this research, we propose a Bayesian dual-network clustering method as a novel way to make these two decisions simultaneously. To accomplish this, the method represents data and parameters as two separate networks with nodes being the unit of analysis (e.g., SKUs). The method then (a) clusters the two networks using a covariate-driven distance function which allows for a high degree of interpretability and (b) infers the data and parameter granularities that offer the best in-sample fit, akin to standard model selection methods. We apply our method to SKU-level demand analysis. The results show that the choices of data and parameter granularities based on our method as compared to those from extant approaches (e.g., latent class analysis) impact the demand elasticities and the optimal pricing of SKUs. We conclude by highlighting the generalizability of our framework to a broad array of marketing problems.

  • Mingyung Kim, Eric Bradlow, Raghuram Iyengar (2022), Selecting Data Granularity and Model Specification Using the Scaled Power Likelihood with Multiple Weights, Marketing Science, 41 (), pp. 848-866.

  • Eric Bradlow, Raghuram Iyengar, Barbara E. Kahn, Jerry (Yoram) Wind (2021), Wharton Marketing: Where Academia Meets Practice, Customer Needs and Solutions , 8 (Customer Needs and Solutions ), pp. 105-109.

    Description: Bradlow, E.T., Iyengar, R., Kahn, B.E. et al. Wharton Marketing: Where Academia Meets Practice,  Customer Needs and Solutions (2021)

  • Qi Yu, Ron Berman, Eric Bradlow (Working), The Dark Side of Adding a Category: Will Existing Ones Pay the Price.

    Abstract: ‘‘More is better’’ has been a belief held by many retailers when they manage product assortments. We challenge this conventional wisdom by demonstrating that a retailer may face more price sensitive demand for existing products when expanding its assortments. To measure the effects of assortment expansion on price sensitivity, we exploit the state of Washington’s privatization of liquor sales in 2012 that generated exogenous variation in retailers’ assortments over time. We find that customers are on average more price sensitive when purchasing from other drink categories after a store started to carry liquor but its impact is heterogeneous. To understand the differential changes in the price sensitivity across product categories depending on whether they are complements or substitutes to the new one, we build a demand model that simultaneously estimates the degree of complementarity between product categories and the changes in price sensitivity upon assortment expansion. We find that the increase in the price sensitivity happens in product categories that are complements to the new one, and that these changes cannot be rationalized by alternative explanations, e.g., correlated preferences across product categories and changes in error variance. Based on the demand estimates, we conduct counterfactual simulations and show that the observed prices are consistent with retailers’ (biased) belief that the price sensitivity does not vary with assortment, which results in significant profit loss.

  • Ludovic Stourm, Raghuram Iyengar, Eric Bradlow (2020), A Flexible Demand Model for Complements Using Household Production Theory, Marketing Science, 39 (), pp. 763-787.

  • Qi Yu, Ron Berman, Eric Bradlow, Pricing Strategy Post Assortment Expansion.

  • Daniel Zantedeschi, Elea McDonnell Feit, Eric Bradlow (2017), Measuring Multi-Channel Advertising Response, Management Science, 63 (8), pp. 2706-2708.

    Abstract: Advances in data collection have made it increasingly easy to collect information on advertising exposures. However, translating this seemingly rich data into measures of advertising response has proven difficult, largely because of concerns that advertisers target customers with a higher propensity to buy or increase advertising during periods of peak demand. We show how this problem can be addressed by studying a setting where a firm randomly held out customers from each campaign, creating a sequence of randomized field experiments that mitigates (many) potential endogeneity problems. Exploratory analysis of individual holdout experiments shows positive effects for both email and catalog; however, the estimated effect for any individual campaign is imprecise, because of the small size of the holdout. To pool data across campaigns, we develop a hierarchical Bayesian model for advertising response that allows us to account for individual differences in purchase propensity and marketing response. Building on the traditional ad-stock framework, we are able to estimate separate decay rates for each advertising medium, allowing us to predict channel-specific short- and long-term effects of advertising and use these predictions to inform marketing strategy. We find that catalogs have substantially longer-lasting impact on customer purchase than emails. We show how the model can be used to score and target individual customers based on their advertising responsiveness, and we find that targeting the most responsive customers increases the predicted returns on advertising by approximately 70% versus traditional recency, frequency, and monetary value–based targeting.

  • Tong Lu, Eric Bradlow, J. Wesley Hutchinson, Binge Consumption of Online Content.

  • Julie Novak, Eleanor McDonnell Feit, Shane T. Jensen, Eric Bradlow (Working), Bayesian Imputation for Anonymous Visits.

  • Valeria Stourm, Eric Bradlow, Peter Fader (2015), Stockpiling Points in Linear Loyalty Programs, Journal of Marketing Research, 52 (2), pp. 253-267.

    Abstract: Customers often stockpile reward points in linear loyalty programs (i.e., programs that do not explicitly reward stockpiling) despite several economic incentives against it (e.g., the time value of money). The authors develop a mathematical model of redemption choice that unites three explanations for why customers seem to be motivated to stockpile on their own, even though the retailer does not reward them for doing so. These motivations are economic (the value of forgone points), cognitive (nonmonetary transaction costs), and psychological (customers value points differently than cash). The authors capture the psychological motivation by allowing customers to book cash and point transactions in separate mental accounts. They estimate the model on data from an international retailer using Markov chain Monte Carlo methods and accurately forecast redemptions during an 11-month out-of-sample period. The results indicate substantial heterogeneity in how customers are motivated to redeem and suggest that the behavior in the data is driven mostly by cognitive and psychological incentives.      


All Courses

  • AMCS5999 - Independent Study

    Independent Study allows students to pursue academic interests not available in regularly offered courses. Students must consult with their academic advisor to formulate a project directly related to the student’s research interests. All independent study courses are subject to the approval of the AMCS Graduate Group Chair.

  • AMCS9999 - Ind Study & Research

    Study under the direction of a faculty member.

  • ECON4999 - Independent Study

    Individual study and research under the direction of a member of the Economics Department faculty. At a minimum, the student must write a major paper summarizing, unifying, and interpreting the results of the study. This is a one semester, one c.u. course.

  • MKTG6110 - Marketing Management

    This course addresses how to design and implement the best combination of marketing efforts to carry out a firm's strategy in its target markets. Specifically, this course seeks to develop the student's (1) understanding of how the firm can benefit by creating and delivering value to its customers, and stakeholders, and (2) skills in applying the analytical concepts and tools of marketing to such decisions as segmentation and targeting, branding, pricing, distribution, and promotion. The course uses lectures and case discussions, case write-ups, student presentations, and a comprehensive final examination to achieve these objectives.

  • MKTG6120 - Dynamic Mktg Strategy

    Building upon Marketing 611, the goal of this course is to develop skills in formulating and implementing marketing strategies for brands and businesses. The course will focus on issues such as the selection of which businesses and segments to compete in, how to allocate resources across businesses, segments, and elements of the marketing mix, as well as other significant strategic issues facing today's managers in a dynamic competitive environment. A central theme of the course is that the answer to these strategic problems varies over time depending on the stage of the product life cycle at which marketing decisions are being made. As such, the PLC serves as the central organizing vehicle of the course. We will explore such issues as how to design optimal strategies for the launch of new products and services that arise during the introductory phase, how to maximize the acceleration of revenue during the growth phase, how to sustain and extend profitability during the mature phase, and how to manage a business during the inevitable decline phase.

  • MKTG8990 - Independent Study

    A student contemplating an independent study project must first find a faculty member who agrees to supervise and approve the student's written proposal as an independent study (MKTG 899). If a student wishes the proposed work to be used to meet the ASP requirement, he/she should then submit the approved proposal to the MBA adviser who will determine if it is an appropriate substitute. Such substitutions will only be approved prior to the beginning of the semester.

  • MKTG9560 - Empirical Models Mktg A

    This course is designed to generate knowledge of the use of quantitative statistical, econometric, and Machine Learning methods and their application to Marketing problems. A strong emphasis is also placed on the applied nature of applying these methods in terms of data requirements, exogenous versus endogenous variation, and computational challenges when using complex models. Students outside of Marketing are welcome, and we discuss how these models can be applied to other disciplines. By the end of the course, students should be familiar with the key issues and approaches in empirical marketing modeling.

  • MKTG9570 - Empirical Models Mktg B

    This course is designed to generate awareness and appreciation of the way several substantive topics in marketing have been studied empirically using quantitative models. This seminar reviews empirical models of marketing phenomena including consumer choice, adoption of new products, sales response to marketing mix elements, and competitive interaction. Applies methods and concepts developed in econometrics and statistics but focuses on substantive issues of model structure and interpretation, rather than on estimation techniques. Ultimately, the goals are a) to prepare students to read and understand the literature and b) to stimulate new research interests. By the end of the course, students should be familiar with the key issues and approaches in empirical marketing modeling.

  • MKTG9950 - Dissertation

  • STAT9950 - Dissertation

Awards and Honors

  • Linback Award for Distinguished PhD Teaching and Mentoring, 2020
  • J.B. Steenkamp Long-Term Impact Award, IJRM Best Paper, 2016
  • Anvil Award, Best Teacher in Wharton MBA Program, 2015
  • Inaugural Fellow of the University of Pennsylvania, 2009
  • Fellow of the American Education Research Association, 2009
  • Finalist, H. Paul Root Award, Best Paper in Journal of Marketing, 2009
  • Finalist, John D.C. Little Best Paper Award, 2008
  • Wharton East WEMBA Teaching Award, 2008
  • American Marketing Association EXPLOR Award, 2007
  • Wharton East WEMBA Teaching Award, 2007
  • Wharton School, MBA Excellence in Teaching Award, 2002, 2003, 2004, 2005, 2006, 2006 Description

    Wharton School, MBA Excellence in Teaching Award

  • NCME Technical or Scientific Contribution to the Field of Educational Measurement: Development of Testlet Response Theory, 2006
  • “Goes Above and Beyond the Call of Duty” Wharton MBA Teaching Award, 2006
  • Wharton School, MBA Excellence in Teaching Award, 2003, 2004, 2005, 2006, 2006
  • Wharton East WEMBA Teaching Award, 2006
  • Fellow of the American Statistical Association, 2005 Description

    Fellow of the American Statistical Association

  • First recipient of The K.P. Chao Professorship, 2005 Description

    Named the first recipient of The K.P. Chao Professorship

  • Helen Kardon Moss Anvil Award Finalist, 2001-2002, 2004-2005, 2005
  • Appointed Fellow of the American Statistical Association, 2005
  • Finalist, Paul E. Green Award for the best paper in Journal of Marketing Research, 2004 Description

    “A Learning-based Model for Imputing Missing Levels in Partial Conjoint Profiles,” co-authored with Y. Hue and T-H Ho, lead article and discussion paper, Vol. XLI (November 2004), 369-38

  • Wharton Undergraduate Excellence in Teaching Award, 2004, 2004
  • AERA Outstanding Reviewer, 2003 Description

    AERA Outstanding Reviewer

  • Wharton West WEMBA Teaching Award, 2003 Description

    Wharton West WEMBA Teaching Award

  • Wharton School, MBA Excellence in Teaching Award, 2005 Description

    2003, 2004, 2005

  • AERA Outstanding Reviewer, 2003
  • Helen Kardon Moss Anvil Award Finalist, 2005 Description

    2001-2002, 2004-2005

  • Miller-Sherrerd MBA Core Teaching Award, 2002 Description

    1999, 2000, 2001, 2002

  • Wharton MBA Core Curriculum Teaching Award, 2001 Description

    1998, 1999, 2001

  • Finalist, American Statistical Association Savage Award Dissertation Prize, 1997 Description

    Finalist, American Statistical Association Savage Award Dissertation Prize

  • Appointed Research Consultant, AT&T Bell Laboratories, 1997 Description

    Appointed Research Consultant, AT&T Bell Laboratories

  • E.I. DuPont de Nemours and Company young researcher award, 1992 Description

    Corporate Marketing Division

  • Harvard University Derek Bok Center for excellence in teaching, 1991 Description

    4-time winner

  • Linback Award for Distinguished PhD Teaching and Mentoring, 1970

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